完善智能体系统：添加疾病分析器模块和项目配置
- 修复基础代理类的导入路径问题和配置引用 - 添加疾病分析器智能体模块 - 实现疾病风险评估和个性化建议功能 - 包含结构化输出模型和智能提示模板 - 新增项目配置文件和包初始化文件 - 更新依赖：添加packaging和sqlalchemy包 - 添加Claude指导文档 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com>
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CLAUDE.md
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CLAUDE.md
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# Claude 指导文档
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## 身份
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你的名字为ycz copilot
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## 要求
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1. 所有的注释必须是中文
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2. 所有的git提交必须是中文
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3. 在构建一个agent（除了base agent）时该agenr的prompt.py中必须根据该agent的response_model.py构建一个示例agent输出（json格式），以确保llm可以依据该格式输出结果。
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4. prompt的description部分主要是介绍这个agent的角色、任务以及目标，给agent一个宏观的概念。
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5. prompt的instruction部分主要是是告诉agent完成任务的步骤，注意事项以及示例agent输出（json格式）
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6. agent.py中build_prompt中的prompt template需要尽可能的简洁，只保留最精华的要求。
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7. 如果需要进行测试需要在测试后删除测试文件
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8. 你需要使用uv作为包管理器，虚拟环境为`.venv`
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- 你可以使用uv run xxx.py 来运行python程序
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- 你可以使用uv add xxx来添加依赖包
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9. 默认的
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agent_system/__init__.py
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agent_system/__init__.py
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# AIM智能体系统初始化文件
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agent_system/base/__init__.py
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agent_system/base/__init__.py
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# 基础类模块初始化文件
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from .agent import BaseAgent
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from .prompt import BasePrompt
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from .response_model import BaseResponseModel
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__all__ = ['BaseAgent', 'BasePrompt', 'BaseResponseModel']
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@ -16,8 +16,8 @@ try:
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except ImportError:
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OLLAMA_AVAILABLE = False
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from agent_system.base import BaseResponseModel
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from config import LLM_CONFIG as llm_config
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from agent_system.base.response_model import BaseResponseModel
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from config import LLM_CONFIG
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class BaseAgent:
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"""基础代理类，封装了 Phidata Agent。
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@ -70,7 +70,7 @@ class BaseAgent:
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self.cache: Optional['Cache'] = Cache() if use_cache else None
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self.agent: Optional[Agent] = None
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self.num_requests = max(1, num_requests) # 确保至少有 1 个请求
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self.llm_config = llm_config or llm_config
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self.llm_config = llm_config or LLM_CONFIG
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# 安全处理默认空列表
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if instructions is None:
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agent_system/disease_analyzer/__init__.py
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agent_system/disease_analyzer/__init__.py
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# 疾病分析智能体模块初始化文件
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from .agent import DiseaseContextAnalyzer
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from .prompt import DiseaseAnalyzerPrompt
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from .response_model import DiseaseAnalysisResult
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__all__ = ['DiseaseContextAnalyzer', 'DiseaseAnalyzerPrompt', 'DiseaseAnalysisResult']
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agent_system/disease_analyzer/agent.py
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agent_system/disease_analyzer/agent.py
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from typing import Dict, Any, List
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from agent_system.base import BaseAgent
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from agent_system.disease_analyzer.prompt import DiseaseAnalyzerPrompt
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from agent_system.disease_analyzer.response_model import DiseaseAnalysisResult
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class DiseaseContextAnalyzer(BaseAgent):
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"""
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疾病上下文分析智能体
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基于患者的现病史和既往史内容，分析疾病类型特点，
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生成初步诊断判断，并确定各子任务的评估重点。
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核心功能:
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1. 识别疾病所属系统（神经、心血管、呼吸、消化等）
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2. 分析起病模式（急性、亚急性、慢性）和严重程度
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3. 推断可能的疾病诊断
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4. 为后续子任务确定针对性的评估重点
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Attributes:
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model_type (str): 使用的大语言模型类型，默认为 deepseek-v3
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llm_config (dict): LLM模型配置参数
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"""
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def __init__(self, model_type: str = "deepseek-v3", llm_config: dict = None):
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"""
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初始化疾病上下文分析智能体
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Args:
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model_type (str): 大语言模型类型，默认使用 deepseek-v3
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llm_config (dict): LLM模型的配置参数，如果为None则使用默认配置
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"""
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super().__init__(
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model_type=model_type,
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description="基于患者主述分析疾病上下文并确定评估重点",
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instructions=DiseaseAnalyzerPrompt.instructions,
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response_model=DiseaseAnalysisResult,
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llm_config=llm_config or {},
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structured_outputs=True,
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markdown=False,
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use_cache=False
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)
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def run(self, hpi_content: str, ph_content: str = "") -> DiseaseAnalysisResult:
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"""
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执行疾病上下文分析
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基于现病史和既往史内容，分析疾病特点，生成初步判断，
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并为后续的现病史收集、既往史收集等子任务确定评估重点。
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Args:
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hpi_content (str): 现病史内容，患者的主要症状描述
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ph_content (str, optional): 既往史内容，患者的历史疾病信息，默认为空字符串
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Returns:
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DiseaseAnalysisResult: 包含疾病分析结果的结构化数据，包括：
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- disease_category: 疾病类别（如神经系统疾病等）
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- suspected_conditions: 可能的诊断列表
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- onset_pattern: 起病模式（急性/亚急性/慢性）
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- severity_level: 疾病严重程度（轻度/中度/重度）
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- evaluation_priorities: 各子任务的评估重点
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- medical_reasoning: 医学分析推理过程
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Raises:
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Exception: 当LLM调用失败时，返回包含默认信息的DiseaseAnalysisResult
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"""
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try:
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# 构建分析提示词
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prompt = self._build_analysis_prompt(hpi_content, ph_content)
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# 调用基类的run方法执行LLM推理
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result = super().run(prompt)
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# 确保返回正确的类型并进行类型转换
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return self._ensure_result_type(result)
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except Exception as e:
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# 当分析失败时记录错误并返回默认结果
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print(f"疾病上下文分析失败: {str(e)}")
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return self._get_fallback_result()
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def _ensure_result_type(self, result: Any) -> DiseaseAnalysisResult:
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"""
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确保返回结果为正确的类型
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Args:
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result (Any): LLM返回的原始结果
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Returns:
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DiseaseAnalysisResult: 转换后的结构化结果
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"""
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if isinstance(result, DiseaseAnalysisResult):
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return result
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elif isinstance(result, dict):
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return DiseaseAnalysisResult(**result)
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else:
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# 如果类型不匹配，返回默认结果
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return self._get_fallback_result()
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def _get_fallback_result(self) -> DiseaseAnalysisResult:
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"""
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生成分析失败时的默认结果
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Returns:
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DiseaseAnalysisResult: 包含默认评估重点的结果
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"""
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return DiseaseAnalysisResult(
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disease_category="未知疾病类型",
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suspected_conditions=["需进一步分析"],
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onset_pattern="未明确",
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severity_level="未评估",
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evaluation_priorities={
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"诊疗经过": ["既往就诊经历", "诊断检查结果", "治疗方案及效果"],
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"主要症状特征": ["症状的具体表现", "症状的严重程度", "症状的持续时间"],
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"伴随症状": ["相关系统症状", "全身性症状", "功能性症状"],
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"病情发展与演变": ["症状变化趋势", "诱发或缓解因素", "病程发展规律"]
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},
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medical_reasoning="由于分析过程中出现异常，系统提供了通用的评估重点，建议人工进一步分析患者病情。"
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)
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def _build_analysis_prompt(self, hpi_content: str, ph_content: str) -> str:
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"""
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构建疾病分析的提示词模板
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根据现病史和既往史内容，构建简洁高效的分析提示词，
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引导LLM进行专业的医学分析和判断。
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Args:
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hpi_content (str): 现病史内容
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ph_content (str): 既往史内容
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Returns:
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str: 精简的分析提示词
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"""
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# 确保既往史内容的合理显示
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past_history_display = ph_content.strip() if ph_content.strip() else "暂无既往史信息"
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# 从prompt类获取示例输出格式
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from agent_system.disease_analyzer.prompt import DiseaseAnalyzerPrompt
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example_output = DiseaseAnalyzerPrompt.get_example_output()
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prompt = f"""患者病史信息：
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现病史: {hpi_content}
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既往史: {past_history_display}
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请分析疾病系统、起病模式、初步诊断，并为关键子任务确定评估重点。
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输出格式示例：
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{example_output}
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请严格按照上述JSON格式输出。"""
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return prompt
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def analyze_patient_chief_complaint(self, chief_complaint: str) -> DiseaseAnalysisResult:
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"""
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基于患者主述进行初步疾病分析的便捷接口
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这是一个专门针对患者主述（chief complaint）的分析方法，
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适用于初诊时仅有患者主述信息的情况。
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Args:
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chief_complaint (str): 患者的主要症状主述
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Returns:
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DiseaseAnalysisResult: 基于主述的初步分析结果
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"""
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return self.run(hpi_content=chief_complaint, ph_content="")
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def get_evaluation_priorities_for_task(self, result: DiseaseAnalysisResult, task_name: str) -> List[str]:
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"""
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获取特定子任务的评估重点
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Args:
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result (DiseaseAnalysisResult): 疾病分析结果
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task_name (str): 子任务名称
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Returns:
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List[str]: 该任务的评估重点列表，如果任务不存在则返回空列表
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"""
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return result.evaluation_priorities.get(task_name, [])
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agent_system/disease_analyzer/prompt.py
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from agent_system.base import BasePrompt
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class DiseaseAnalyzerPrompt(BasePrompt):
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"""
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疾病上下文分析智能体的提示词模板
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定义了疾病分析智能体的角色、任务目标和执行指令，
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确保智能体能够根据患者主述生成专业的初步医学判断。
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"""
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# 智能体角色和目标描述
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description = (
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"你是一名专业的疾病上下文分析医师，擅长基于患者主述进行初步的医学分析和判断。"
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"你的主要任务是根据患者的现病史和既往史，分析疾病特点，推断可能的诊断，"
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"并为后续的专项病史收集任务提供针对性的评估重点指导。"
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"你的分析将为医生后续的诊疗决策提供重要参考。"
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)
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# 执行指令和注意事项
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instructions = [
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"## 核心分析任务",
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"1. **疾病系统识别**: 基于症状特点，准确判断疾病所属的主要系统（神经、心血管、呼吸、消化、泌尿、内分泌、骨科等）",
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"2. **起病特征分析**: 结合症状出现的时间进程和症状严重程度，准确评估起病模式（急性/亚急性/慢性）",
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"3. **初步诊断推断**: 运用临床医学知识，按照可能性大小排序，提出2-4个最可能的诊断假设",
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"4. **评估重点制定**: 针对推断的疾病类型，为关键子任务确定具体的评估重点和收集方向",
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"",
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"## 重点子任务评估指导",
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"- **诊疗经过**: 根据疾病特点，确定最关键的诊疗信息收集重点（如检查结果、治疗反应等）",
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"- **主要症状特征**: 针对核心症状，确定需要深入探究的具体特征细节",
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"- **伴随症状**: 基于疾病的病理生理特点，识别可能的相关症状表现",
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"- **病情发展与演变**: 关注疾病的发展规律、诱发因素和缓解因素",
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"",
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"## 输出要求和质量标准",
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"1. **格式要求**: 严格按照 DiseaseAnalysisResult 的 JSON 结构输出，不得省略任何必需字段",
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"2. **内容质量**: 评估重点必须具体明确、具有可操作性，避免泛泛而谈的描述",
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"3. **医学专业性**: 基于循证医学证据和临床最佳实践，考虑疾病的病理生理机制",
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"4. **实用性**: 重视临床实用性，确保评估重点能够有效指导后续的专项病史收集工作",
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"",
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"## 示例输出格式（JSON）",
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"{",
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" \"disease_category\": \"神经系统疾病\",",
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" \"suspected_conditions\": [\"偶发性头痛\", \"紧张性头痛\", \"丘脑下部功能异常\"],",
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" \"onset_pattern\": \"亚急性\",",
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" \"severity_level\": \"中度\",",
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" \"evaluation_priorities\": {",
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" \"诊疗经过\": [\"头部CT/MRI检查结果\", \"镇痛药物使用及效果\", \"神经内科就诊情况\"],",
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" \"主要症状特征\": [\"头痛的具体部位和性质\", \"头痛的发作频率和持续时间\", \"头痛的严重程度评估\"],",
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" \"伴随症状\": [\"是否伴有恶心呕吐\", \"是否有视物模糊或复视\", \"是否存在睡眠障碍\"],",
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" \"病情发展与演变\": [\"头痛的诱发因素分析\", \"头痛的缓解方式和程度\", \"病情的发展趋势和周期性\"]",
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" },",
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" \"medical_reasoning\": \"患者主述为反复性头痛，结合起病特点和症状表现，首先考虑原发性头痛疾病。需进一步收集头痛的具体特征、诱发因素和伴随症状，同时关注既往检查和治疗情况，以明确诊断和制定下一步诊疗计划。\"",
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"}"
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]
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@staticmethod
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def get_example_output() -> str:
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"""
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获取示例输出格式，用于指导 LLM 生成符合要求的结构化输出
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Returns:
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str: JSON 格式的示例输出
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"""
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return """{
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"disease_category": "疾病类别（如神经系统疾病）",
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"suspected_conditions": ["可能诊断1", "可能诊断2", "可能诊断3"],
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"onset_pattern": "起病模式（急性/亚急性/慢性）",
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"severity_level": "严重程度（轻度/中度/重度）",
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"evaluation_priorities": {
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"诊疗经过": ["重点信息1", "重点信息2", "重点信息3"],
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"主要症状特征": ["重点特征1", "重点特征2", "重点特征3"],
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"伴随症状": ["重点症状1", "重点症状2", "重点症状3"],
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"病情发展与演变": ["重点发展1", "重点发展2", "重点发展3"]
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},
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"medical_reasoning": "详细的医学分析推理过程，包括诊断依据和评估重点的制定理由"
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}"""
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32
agent_system/disease_analyzer/response_model.py
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agent_system/disease_analyzer/response_model.py
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from typing import List, Dict, Any
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from pydantic import Field
|
||||
from agent_system.base import BaseResponseModel
|
||||
|
||||
class DiseaseAnalysisResult(BaseResponseModel):
|
||||
"""
|
||||
疾病上下文分析结果模型
|
||||
"""
|
||||
disease_category: str = Field(
|
||||
...,
|
||||
description="疾病类别（如：神经系统疾病、心血管疾病、呼吸系统疾病等）"
|
||||
)
|
||||
suspected_conditions: List[str] = Field(
|
||||
default_factory=list,
|
||||
description="可能的疾病诊断列表"
|
||||
)
|
||||
onset_pattern: str = Field(
|
||||
...,
|
||||
description="起病模式（急性、亚急性、慢性）"
|
||||
)
|
||||
severity_level: str = Field(
|
||||
...,
|
||||
description="疾病严重程度（轻度、中度、重度）"
|
||||
)
|
||||
evaluation_priorities: Dict[str, List[str]] = Field(
|
||||
default_factory=dict,
|
||||
description="各子任务的评估重点，key为子任务名称，value为重点因素列表"
|
||||
)
|
||||
medical_reasoning: str = Field(
|
||||
...,
|
||||
description="医学分析推理过程"
|
||||
)
|
||||
88
config.py
Normal file
88
config.py
Normal file
@ -0,0 +1,88 @@
|
||||
import os
|
||||
|
||||
API_KEY = "sk-263038d4bf4e46a0bed16532587cff40"
|
||||
|
||||
# {project_root}/medsynthai
|
||||
BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
|
||||
|
||||
# LLM model configuration based on agno
|
||||
LLM_CONFIG = {
|
||||
"deepseek": {
|
||||
"class": "DeepSeek",
|
||||
"params": {
|
||||
"id": "deepseek-chat",
|
||||
"api_key": API_KEY,
|
||||
"base_url": "https://api.deepseek.com"
|
||||
}
|
||||
},
|
||||
"ollama": {
|
||||
"class": "Ollama",
|
||||
"params": {
|
||||
"id": "qwen2.5:latest",
|
||||
"host": "127.0.0.1"
|
||||
}
|
||||
},
|
||||
"deepseek-v3": {
|
||||
"class": "OpenAILike",
|
||||
"params": {
|
||||
"id": "deepseek-v3",
|
||||
"base_url": "https://dashscope.aliyuncs.com/compatible-mode/v1",
|
||||
"api_key": API_KEY
|
||||
}
|
||||
},
|
||||
"deepseek-r1": {
|
||||
"class": "OpenAILike",
|
||||
"params": {
|
||||
"id": "deepseek-r1",
|
||||
"base_url": "https://dashscope.aliyuncs.com/compatible-mode/v1",
|
||||
"api_key": API_KEY
|
||||
}
|
||||
},
|
||||
"qwen-max": {
|
||||
"class": "OpenAILike",
|
||||
"params": {
|
||||
"id": "qwen-max",
|
||||
"base_url": "https://dashscope.aliyuncs.com/compatible-mode/v1",
|
||||
"api_key": API_KEY
|
||||
}
|
||||
},
|
||||
"qwen-vl-max": {
|
||||
"class": "OpenAILike", # 使用OpenAI兼容类
|
||||
"params": {
|
||||
"id": "qwen-vl-max",
|
||||
"base_url": "https://dashscope.aliyuncs.com/compatible-mode/v1", # OpenAI兼容端点 # 降低随机性，提高结果一致性
|
||||
"api_key": API_KEY
|
||||
}
|
||||
},
|
||||
"aliyun": {
|
||||
"class": "OpenAILike",
|
||||
"params": {
|
||||
"id": "qwen-max", # 默认使用qwen-max模型
|
||||
"base_url": "https://dashscope.aliyuncs.com/compatible-mode/v1",
|
||||
"api_key": API_KEY
|
||||
}
|
||||
}
|
||||
|
||||
}
|
||||
|
||||
AGENT_CONFIG = {
|
||||
"medical_image_analysis_agent": "qwen-vl-max"
|
||||
}
|
||||
|
||||
RAG_CONFIG = {
|
||||
"lightrag": {
|
||||
"working_dir": "./Vector_DB_Med",
|
||||
"tokenizer_name": "trueto/medbert-base-chinese",
|
||||
"model_name": "trueto/medbert-base-chinese",
|
||||
"embedding_dim": 768,
|
||||
"max_token_size": 512
|
||||
},
|
||||
"chroma_db": {
|
||||
"api_key": API_KEY,
|
||||
"base_url": "https://dashscope.aliyuncs.com/compatible-mode/v1",
|
||||
"collection_name": "doctor",
|
||||
"batch_size": 100,
|
||||
"chroma_db_path": os.path.join(BASE_DIR, "static/rag/chroma_db"),
|
||||
"csv_path": os.path.join(BASE_DIR, "static/files/zhongkang_doctor_list.csv")
|
||||
}
|
||||
}
|
||||
@ -8,5 +8,7 @@ dependencies = [
|
||||
"agno>=1.7.9",
|
||||
"ollama>=0.5.3",
|
||||
"openai>=1.99.6",
|
||||
"packaging>=25.0",
|
||||
"pydantic>=2.11.7",
|
||||
"sqlalchemy>=2.0.42",
|
||||
]
|
||||
|
||||
58
uv.lock
generated
58
uv.lock
generated
@ -33,7 +33,9 @@ dependencies = [
|
||||
{ name = "agno" },
|
||||
{ name = "ollama" },
|
||||
{ name = "openai" },
|
||||
{ name = "packaging" },
|
||||
{ name = "pydantic" },
|
||||
{ name = "sqlalchemy" },
|
||||
]
|
||||
|
||||
[package.metadata]
|
||||
@ -41,7 +43,9 @@ requires-dist = [
|
||||
{ name = "agno", specifier = ">=1.7.9" },
|
||||
{ name = "ollama", specifier = ">=0.5.3" },
|
||||
{ name = "openai", specifier = ">=1.99.6" },
|
||||
{ name = "packaging", specifier = ">=25.0" },
|
||||
{ name = "pydantic", specifier = ">=2.11.7" },
|
||||
{ name = "sqlalchemy", specifier = ">=2.0.42" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
@ -138,6 +142,30 @@ wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/01/61/d4b89fec821f72385526e1b9d9a3a0385dda4a72b206d28049e2c7cd39b8/gitpython-3.1.45-py3-none-any.whl", hash = "sha256:8908cb2e02fb3b93b7eb0f2827125cb699869470432cc885f019b8fd0fccff77", size = 208168, upload-time = "2025-07-24T03:45:52.517Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "greenlet"
|
||||
version = "3.2.4"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/03/b8/704d753a5a45507a7aab61f18db9509302ed3d0a27ac7e0359ec2905b1a6/greenlet-3.2.4.tar.gz", hash = "sha256:0dca0d95ff849f9a364385f36ab49f50065d76964944638be9691e1832e9f86d", size = 188260, upload-time = "2025-08-07T13:24:33.51Z" }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/49/e8/58c7f85958bda41dafea50497cbd59738c5c43dbbea5ee83d651234398f4/greenlet-3.2.4-cp313-cp313-macosx_11_0_universal2.whl", hash = "sha256:1a921e542453fe531144e91e1feedf12e07351b1cf6c9e8a3325ea600a715a31", size = 272814, upload-time = "2025-08-07T13:15:50.011Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/62/dd/b9f59862e9e257a16e4e610480cfffd29e3fae018a68c2332090b53aac3d/greenlet-3.2.4-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:cd3c8e693bff0fff6ba55f140bf390fa92c994083f838fece0f63be121334945", size = 641073, upload-time = "2025-08-07T13:42:57.23Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/f7/0b/bc13f787394920b23073ca3b6c4a7a21396301ed75a655bcb47196b50e6e/greenlet-3.2.4-cp313-cp313-manylinux2014_ppc64le.manylinux_2_17_ppc64le.whl", hash = "sha256:710638eb93b1fa52823aa91bf75326f9ecdfd5e0466f00789246a5280f4ba0fc", size = 655191, upload-time = "2025-08-07T13:45:29.752Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/f2/d6/6adde57d1345a8d0f14d31e4ab9c23cfe8e2cd39c3baf7674b4b0338d266/greenlet-3.2.4-cp313-cp313-manylinux2014_s390x.manylinux_2_17_s390x.whl", hash = "sha256:c5111ccdc9c88f423426df3fd1811bfc40ed66264d35aa373420a34377efc98a", size = 649516, upload-time = "2025-08-07T13:53:16.314Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/7f/3b/3a3328a788d4a473889a2d403199932be55b1b0060f4ddd96ee7cdfcad10/greenlet-3.2.4-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:d76383238584e9711e20ebe14db6c88ddcedc1829a9ad31a584389463b5aa504", size = 652169, upload-time = "2025-08-07T13:18:32.861Z" },
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||||
{ url = "https://files.pythonhosted.org/packages/ee/43/3cecdc0349359e1a527cbf2e3e28e5f8f06d3343aaf82ca13437a9aa290f/greenlet-3.2.4-cp313-cp313-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:23768528f2911bcd7e475210822ffb5254ed10d71f4028387e5a99b4c6699671", size = 610497, upload-time = "2025-08-07T13:18:31.636Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/b8/19/06b6cf5d604e2c382a6f31cafafd6f33d5dea706f4db7bdab184bad2b21d/greenlet-3.2.4-cp313-cp313-musllinux_1_1_aarch64.whl", hash = "sha256:00fadb3fedccc447f517ee0d3fd8fe49eae949e1cd0f6a611818f4f6fb7dc83b", size = 1121662, upload-time = "2025-08-07T13:42:41.117Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/a2/15/0d5e4e1a66fab130d98168fe984c509249c833c1a3c16806b90f253ce7b9/greenlet-3.2.4-cp313-cp313-musllinux_1_1_x86_64.whl", hash = "sha256:d25c5091190f2dc0eaa3f950252122edbbadbb682aa7b1ef2f8af0f8c0afefae", size = 1149210, upload-time = "2025-08-07T13:18:24.072Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/0b/55/2321e43595e6801e105fcfdee02b34c0f996eb71e6ddffca6b10b7e1d771/greenlet-3.2.4-cp313-cp313-win_amd64.whl", hash = "sha256:554b03b6e73aaabec3745364d6239e9e012d64c68ccd0b8430c64ccc14939a8b", size = 299685, upload-time = "2025-08-07T13:24:38.824Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/22/5c/85273fd7cc388285632b0498dbbab97596e04b154933dfe0f3e68156c68c/greenlet-3.2.4-cp314-cp314-macosx_11_0_universal2.whl", hash = "sha256:49a30d5fda2507ae77be16479bdb62a660fa51b1eb4928b524975b3bde77b3c0", size = 273586, upload-time = "2025-08-07T13:16:08.004Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/d1/75/10aeeaa3da9332c2e761e4c50d4c3556c21113ee3f0afa2cf5769946f7a3/greenlet-3.2.4-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:299fd615cd8fc86267b47597123e3f43ad79c9d8a22bebdce535e53550763e2f", size = 686346, upload-time = "2025-08-07T13:42:59.944Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/c0/aa/687d6b12ffb505a4447567d1f3abea23bd20e73a5bed63871178e0831b7a/greenlet-3.2.4-cp314-cp314-manylinux2014_ppc64le.manylinux_2_17_ppc64le.whl", hash = "sha256:c17b6b34111ea72fc5a4e4beec9711d2226285f0386ea83477cbb97c30a3f3a5", size = 699218, upload-time = "2025-08-07T13:45:30.969Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/dc/8b/29aae55436521f1d6f8ff4e12fb676f3400de7fcf27fccd1d4d17fd8fecd/greenlet-3.2.4-cp314-cp314-manylinux2014_s390x.manylinux_2_17_s390x.whl", hash = "sha256:b4a1870c51720687af7fa3e7cda6d08d801dae660f75a76f3845b642b4da6ee1", size = 694659, upload-time = "2025-08-07T13:53:17.759Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/92/2e/ea25914b1ebfde93b6fc4ff46d6864564fba59024e928bdc7de475affc25/greenlet-3.2.4-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:061dc4cf2c34852b052a8620d40f36324554bc192be474b9e9770e8c042fd735", size = 695355, upload-time = "2025-08-07T13:18:34.517Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/72/60/fc56c62046ec17f6b0d3060564562c64c862948c9d4bc8aa807cf5bd74f4/greenlet-3.2.4-cp314-cp314-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:44358b9bf66c8576a9f57a590d5f5d6e72fa4228b763d0e43fee6d3b06d3a337", size = 657512, upload-time = "2025-08-07T13:18:33.969Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/e3/a5/6ddab2b4c112be95601c13428db1d8b6608a8b6039816f2ba09c346c08fc/greenlet-3.2.4-cp314-cp314-win_amd64.whl", hash = "sha256:e37ab26028f12dbb0ff65f29a8d3d44a765c61e729647bf2ddfbbed621726f01", size = 303425, upload-time = "2025-08-07T13:32:27.59Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "h11"
|
||||
version = "0.16.0"
|
||||
@ -273,6 +301,15 @@ wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/d6/dd/9aa956485c2856346b3181542fbb0aea4e5b457fa7a523944726746da8da/openai-1.99.6-py3-none-any.whl", hash = "sha256:e40d44b2989588c45ce13819598788b77b8fb80ba2f7ae95ce90d14e46f1bd26", size = 786296, upload-time = "2025-08-09T15:20:51.95Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "packaging"
|
||||
version = "25.0"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/a1/d4/1fc4078c65507b51b96ca8f8c3ba19e6a61c8253c72794544580a7b6c24d/packaging-25.0.tar.gz", hash = "sha256:d443872c98d677bf60f6a1f2f8c1cb748e8fe762d2bf9d3148b5599295b0fc4f", size = 165727, upload-time = "2025-04-19T11:48:59.673Z" }
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||||
wheels = [
|
||||
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||||
]
|
||||
|
||||
[[package]]
|
||||
name = "pydantic"
|
||||
version = "2.11.7"
|
||||
@ -414,6 +451,27 @@ wheels = [
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||||
{ url = "https://files.pythonhosted.org/packages/e9/44/75a9c9421471a6c4805dbf2356f7c181a29c1879239abab1ea2cc8f38b40/sniffio-1.3.1-py3-none-any.whl", hash = "sha256:2f6da418d1f1e0fddd844478f41680e794e6051915791a034ff65e5f100525a2", size = 10235, upload-time = "2024-02-25T23:20:01.196Z" },
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||||
]
|
||||
|
||||
[[package]]
|
||||
name = "sqlalchemy"
|
||||
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|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
dependencies = [
|
||||
{ name = "greenlet", marker = "(python_full_version < '3.14' and platform_machine == 'AMD64') or (python_full_version < '3.14' and platform_machine == 'WIN32') or (python_full_version < '3.14' and platform_machine == 'aarch64') or (python_full_version < '3.14' and platform_machine == 'amd64') or (python_full_version < '3.14' and platform_machine == 'ppc64le') or (python_full_version < '3.14' and platform_machine == 'win32') or (python_full_version < '3.14' and platform_machine == 'x86_64')" },
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{ name = "typing-extensions" },
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]
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||||
|
||||
[[package]]
|
||||
name = "tomli"
|
||||
version = "2.2.1"
|
||||
|
||||
Loading…
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Reference in New Issue
Block a user